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Record W3175810652 · doi:10.1111/cag.12702

Wildfire risk and response in Jasper National Park, Alberta: Application of an adaptation readiness framework

2021· article· en· W3175810652 on OpenAlexvenueaboutno aff
Rechelle Halabut, S. Jeff Birchall

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsPreparednessNational parkStakeholderAdaptation (eye)Environmental resource managementClimate changeEnvironmental planningGeographyClimate change adaptationPolitical scienceEnvironmental scienceEcologyPublic relationsPsychology

Abstract

fetched live from OpenAlex

Environmental change associated with warmer temperatures is creating unprecedented conditions in natural regions and ecosystems. In Jasper National Park, Alberta, climate change, historical fire management practices, and the mountain pine beetle infestation are combining to increase the risk of a major wildfire. The intent of this short viewpoint is to provide a primer for decision makers. The application of an adaptation readiness framework highlights areas where the Park's level of preparedness for wildfire adaptation is well developed and areas in need of further attention. Areas where adaptation is well developed include political leadership, decision making, and stakeholder engagement. Further attention is necessary around institutional organizations, funding, and public support.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.190
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2021
Admission routes2
Has abstractyes

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Same venueCanadian Geographies / Géographies canadiennesSame topicFire effects on ecosystemsFrench-language works237,207